{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import d2lzh as d2l\n",
    "from mxnet import autograd, nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_inputs = 784\n",
    "num_outputs = 10\n",
    "\n",
    "W = nd.random.normal(scale=0.01, shape=(num_inputs, num_outputs))\n",
    "b = nd.zeros(num_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算梯度\n",
    "W.attach_grad()\n",
    "b.attach_grad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n实现Softmax运算\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "实现Softmax运算\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\n",
       " [[5. 7. 9.]]\n",
       " <NDArray 1x3 @cpu(0)>,\n",
       " \n",
       " [[ 6.]\n",
       "  [15.]]\n",
       " <NDArray 2x1 @cpu(0)>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "如何对多维NDArray按维度操作\n",
    "给定⼀个NDArray矩阵X。我们可以只对其中同⼀列（axis=0）或同⼀⾏（axis=1）\n",
    "的元素求和，并在结果中保留⾏和列这两个维度（keepdims=True）\n",
    "'''\n",
    "X = nd.array([[1,2,3],[4,5,6]])\n",
    "X.sum(axis=0, keepdims=True), X.sum(axis=1,keepdims=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\n",
       " [5. 7. 9.]\n",
       " <NDArray 3 @cpu(0)>,\n",
       " \n",
       " [ 6. 15.]\n",
       " <NDArray 2 @cpu(0)>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对比上面\n",
    "X.sum(axis=0), X.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(X):\n",
    "    X_exp = X.exp()\n",
    "    partition = X_exp.sum(axis=1, keepdims=True)\n",
    "    return X_exp / partition # 这里应用了广播机制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\n",
       " [[0.6264712  0.126293   0.01826552 0.10885343 0.12011679]\n",
       "  [0.25569436 0.2917251  0.07546549 0.3024068  0.07470828]]\n",
       " <NDArray 2x5 @cpu(0)>,\n",
       " \n",
       " [0.99999994 1.        ]\n",
       " <NDArray 2 @cpu(0)>,\n",
       " \n",
       " [0.88216555 0.4180181  0.09373102 0.41126022 0.19482507]\n",
       " <NDArray 5 @cpu(0)>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "axis=1是整行进行相加\n",
    "axis=0是列对列进行相加'''\n",
    "X = nd.random.normal(shape=(2,5))\n",
    "X_prob = softmax(X)\n",
    "X_prob, X_prob.sum(axis=1), X_prob.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "定义模型\n",
    "'''\n",
    "def net(X):\n",
    "    return softmax(nd.dot(X.reshape((-1, num_inputs)), W) + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[0.1 0.5]\n",
       "<NDArray 2 @cpu(0)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "通过使⽤pick函数，我们得到了2个样本的标签的预测概率\n",
    "'''\n",
    "y_hat = nd.array([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\n",
    "y = nd.array([0,2], dtype='int32')\n",
    "nd.pick(y_hat, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "实现softmax回归的交叉熵损失函数\n",
    "交叉熵越高代表越能够表现出这个特征对于这个标签的显著性\n",
    "对于softmax模型来说（对于单个分类的数据）\n",
    "得到一个显著的概率就可以表示一个特征代表的一个标签\n",
    "这是一个梯度上升的过程\n",
    "但是mxnet的模型训练的过程中使用的是梯度下降的算法，所以对其求它的相反数\n",
    "将梯度上升的问题转化为一个梯度下降的问题\n",
    "'''\n",
    "def cross_entropy(y_hat, y):\n",
    "    return -nd.pick(y_hat, y).log()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 2.], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "计算分类准确率\n",
    "'''\n",
    "# y_hat 的类型为32位的浮点型的 NDArray\n",
    "y_hat.argmax(axis=1).asnumpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(y_hat, y):\n",
    "    return (y_hat.argmax(axis=1) == y.astype('float32')).mean().asscalar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy(y_hat, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_accarcy(data_iter, net):\n",
    "    acc_sum, n = 0.0, 0\n",
    "    for X, y in data_iter:\n",
    "        y = y.astype('float32')\n",
    "        acc_sum += (net(X).argmax(axis=1) == y).sum().asscalar()\n",
    "        n += y.size\n",
    "    return acc_sum / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0856"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_accarcy(test_iter, net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss 0.7044, train acc 0.770, test acc 0.805\n",
      "epoch 2, loss 0.5669, train acc 0.813, test acc 0.823\n",
      "epoch 3, loss 0.5257, train acc 0.824, test acc 0.835\n",
      "epoch 4, loss 0.5036, train acc 0.831, test acc 0.840\n",
      "epoch 5, loss 0.4877, train acc 0.836, test acc 0.841\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "训练模型\n",
    "'''\n",
    "num_epochs, lr = 5, 0.1\n",
    "def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params=None,\n",
    "             lr=None, trainer=None):\n",
    "    for epoch in range(num_epochs):\n",
    "        train_l_sum, train_acc_sum, n = 0.0, 0.0, 0\n",
    "        for X, y in train_iter:\n",
    "            with autograd.record():\n",
    "                y_hat = net(X)\n",
    "                l = loss(y_hat, y).sum()\n",
    "            l.backward()\n",
    "            if trainer is None:\n",
    "                d2l.sgd(params, lr, batch_size)\n",
    "            else:\n",
    "                trainer.step(batch_size)\n",
    "            y = y.astype('float32')\n",
    "            train_l_sum += l.asscalar()\n",
    "            train_acc_sum += (y_hat.argmax(axis=1) == y).sum().asscalar()\n",
    "            n += y.size\n",
    "        test_acc = evaluate_accarcy(test_iter, net)\n",
    "        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f' % (epoch + 1,\n",
    "              train_l_sum / n, train_acc_sum / n, test_acc))\n",
    "train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, batch_size, [W, b], lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for X, y in test_iter:\n",
    "    break\n",
    "true_labels = d2l.get_fashion_mnist_labels(y.asnumpy())\n",
    "pred_labels = d2l.get_fashion_mnist_labels(net(X).argmax(axis=1).asnumpy())\n",
    "titles = [true + '\\n' + pred for true, pred in zip(true_labels, pred_labels)]\n",
    "\n",
    "d2l.show_fashion_mnist(X[0:9], titles[0:9])"
   ]
  }
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